Assessment of weather effect on flower morphogenesis and fruit set in mango varieties in central India

A two-year field experiment was initiated in 2017-18 and 2018-19 years simultaneously to assess temperature on flower morphogenesis stages, flower sex ratio (hermaphrodite/staminate male flower) and fruit set in monoembryonic Langra and Amrapali varieties. Different dates of flower phenological stages viz., bud, panicle, bloom and flower initiation, pea, marble, egg, and maturity of fruits were recorded. The Langra variety exhibited bud initiation after mid December whereas Amrapali variety by the end of December. The range of mean maximum / minimum temperature as 26-31/10-12 °C promoted hermaphrodite flowers per panicle by 74 per cent in Langra variety, whereas range of 27-29/11-13 °C favored by 35 per cent in Amrapali variety. A positive and significant correlation between total number of flower / panicle and flower sex ratio in both the varieties suggested that higher temperature during initial flower phenologies improved number of hermaphrodite flowers. A mean minimum temperature for producing more number of hermaphrodite flower exhibited a range of 11-14 °C under central Indian conditions. Fruit set was maximum during pea stage and decline afterwards in marble and fruit maturity stages due to sudden rise in temperature at marble stage that caused in drop down of humidity thereby resulted in fruit drop in both the varieties.

Extreme learning machines for weather-based modelling of silk cocoon production

In spite of the immense popularity and sheer power of the neural network models, their application in sericulture is still very much limited. With this backdrop, this study evaluates the suitability of neural network models in comparison with the linear regression models in predicting silk cocoon production of the selected six districts (Kolar, Chikballapur, Ramanagara, Chamarajanagar, Mandya and Mysuru) of Karnataka by utilising weather variables for ten consecutive years (2009-2018). As the weather variables are found to be correlated, principal components are obtained and fed into the linear (principal component regression) and non-linear models (back propagation-artificial neural network and extreme learning machine) as inputs. Outcomes emanated from this experiment have revealed the clear advantages of employing extreme learning machines (ELMs) for weather-based modelling of silk cocoon production. Application of ELM would be particularly useful, when the relation between production and its attributing characters is complex and non-linear.

Effect of elevated carbon dioxide on biology and morphometric parameters of yellow stem borer, Scirpophaga incertulas infesting rice (Oryza sativa)

An experiment was conducted at Research Farm, National Rice Research Institute, Cuttack, Odisha, India to quantify the effect of elevated carbon dioxide (CO2) concentrations on the biology and morphometric parameters of yellow stem borer (Scirpophaga incertulas, Pyralidae, Lepidoptera). Yellow stem borer is one of the major pest of rice in the whole rice growing regions of South East Asia. The effect of three carbon dioxide concentrations i.e. 410 ppm (ambient), 550 ppm and 700 ppm on the duration of the developmental period as well as morphometric parameters of each stage of the lifecycle of the pest was analysed. It was found that, there was an increase in the duration of the developmental period of each stage of life cycle as the concentration of CO2 increases. However, the life span of the adult moth was significantly lower under the elevated CO2 concentrations when compared with ambient CO2 concentration. Morphometric parameters viz., mean length, width and weight of each larval instar, pupa and adult were found to be significantly higher in elevated concentrations of CO2 as compared to ambient concentration.

Evaluating radiation interception pattern and RUE of green gram grown in Lower Gangetic Plains and assessing future yield based on RUE

An experiment was conducted in the Lower Gangetic Plains of West Bengal during 2017 and 2018 with three popular green gram varieties of the region (viz. Samrat, PM05 and Meha). Along with studying the variation of PAR components, a radiation use efficiency (RUE) based equation irrespective of varieties was developed and used to estimate the green gram yield for 2040-2090 period under RCP 4.5 and 8.5 scenarios. Field experimental results showed that almost 33.33 to 52.12% higher yield was recorded in 2017 in comparison to 2018. As observed through pooled experimental data of two years, PM05 produced 3 to 4% higher pod and 4 to 15% more biomass than Samrat and Meha with the highest radiation use efficiency (1.786 g MJ-1). Results also depicted that enhanced thermal condition would cause 9 to 15 days of advancement in maturity. Biomass and yield would also decrease gradually from 2040 to 2090 with an average rate of 7.60-11.70% and 10.19-14.17% respectively. The supporting literature confirms that future yield prediction under projected climate based on “radiation to biomass” conversion efficiency can be used successfully as a method to evaluate climate change impact on crop performance.

Wheat yield prediction based on weather parameters using multiple linear, neural network and penalised regression models

Wheat yield production is largely attributed by weather parameters. Model developed by multiple linear, neural network and penalised regression techniques using weather data have the potential to provide reliable, timely and cost-effective prediction of wheat yield. Wheat yield data and weather parameter during crop growing period (46th to 15th SMW) for more than 30 years were collected for study area and model was developed using stepwise multiple linear regression (SMLR), principal component analysis (PCA) in combination with SMLR, artificial neural network (ANN) alone and in combination with PCA, least absolute shrinkage and selection operator (LASSO) and elastic net (ENET) techniques. Analysis was carried out by fixing 70% of the data for calibration and remaining dataset for validation. On examining these models, LASSO and elastic net are performing excellent having nRMSE value less than 10 % for four out of five location and good for one location, because of prevention in over fitting and reducing regression coefficient by penalization.